A water distribution network of water supply is divided into smaller areas referred to as district meter areas (DMAs). In other words, the water distribution network comprises a plurality of DMAs neighboring one another. Flow rate sensors including: a DMA flowmeter; and a flowmeter and a pressure regulating valve (PRV) are arranged at entrances and exits of the respective DMAs. Thus, a water use amount per unit time period [liter/hour] in each DMA can be grasped by measuring flow rates [liter/second] of water flowing into each DMA and flowing out from each DMA by using those flow rate sensors. Each DMA is hereinafter referred to as “monitor area”, “measurement area”, or simply “area”.
As widely known, an actual water use amount varies depending on a measurement time zone (whether it is daytime or nighttime), and changes in a time series. Moreover, the actual water use amount changes depending on a measurement day (whether it is a weekday or a holiday (including Saturday, Sunday, or a national holiday). Further, the actual water use amount changes depending on weather including a temperature and a rainfall. In other words, the actual water use amount changes depending on environment conditions (e.g., the outdoor temperature, the day of week, and the time zone) in the monitor area at that time instant.
Thus, it is conceivable to accumulate past flow rate data representing the water use amount measured in the past in the memory, calculate a predicted value of a current water use amount based on the accumulated data, compare the predicted value and an actual measurement value of the current water use amount with each other, and determine whether or not a water leakage has occurred in the monitor area based on a result of the comparison (a predicted error). In that case, as a material (indicator) for determining whether or not a water leakage exists, a probability (water-leakage score) of occurrence of a water leakage in this monitor area may be produced.
Incidentally, in order to calculate the predicted value of the current water use amount, it is necessary to accumulate an enormous amount of past flow rate data (big data) representing the water use amount measured in the past as the accumulated data in the memory, and to build a prediction model (prediction equation for the water use amount) from the accumulated data through learning.
Meanwhile, as one method of learning the prediction model, there is known heterogeneous mixture learning (HML), which is an analysis technology for heterogeneous mixture data. As one specific example of the heterogeneous mixture learning, there is factorized asymptotic Bayesian inference (FAB) (see, for example, Non Patent Document 1).
Moreover, in Patent Document 1, there is proposed a water-leakage determination method involving comparing a minimum water distribution amount and an allowable water-leakage amount acquired when no leakage exists in water pipes, and determining that a water leakage exists when the minimum water distribution amount is more than the allowable water-leakage amount.